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from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper |
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from langchain import OpenAI |
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import gradio as gr |
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import sys |
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import os |
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os.environ["OPENAI_API_KEY"] = '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' |
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def construct_index(directory_path): |
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max_input_size = 4096 |
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num_outputs = 512 |
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max_chunk_overlap = 20 |
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chunk_size_limit = 600 |
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prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) |
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llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7, model_name="text-davinci-003", max_tokens=num_outputs)) |
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documents = SimpleDirectoryReader(directory_path).load_data() |
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index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) |
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index.save_to_disk('index.json') |
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return index |
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def chatbot(input_text): |
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index = GPTSimpleVectorIndex.load_from_disk('index.json') |
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response = index.query(input_text, response_mode="compact") |
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return response.response |
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iface = gr.Interface(fn=chatbot, |
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inputs=gr.inputs.Textbox(lines=3, label="O quê gostaria de saber?"), |
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outputs=gr.inputs.Textbox(lines=3, label="Veja o que encontrei"), |
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title="CD2 IA") |
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index = construct_index("docs") |
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iface.launch(share=True) |
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